{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from scipy import stats\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import Sequential, layers, callbacks\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, Bidirectional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
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       "      <th>Datetime</th>\n",
       "      <th>Battery Voltage</th>\n",
       "      <th>Hammer Sensor Hole and System State</th>\n",
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      "text/plain": [
       "             Datetime  Battery Voltage  Hammer Sensor Hole and System State  \\\n",
       "0 2021-12-01 17:37:00              NaN                                  NaN   \n",
       "1 2021-12-01 17:41:00              NaN                                  NaN   \n",
       "2 2021-12-01 17:42:00              NaN                                  NaN   \n",
       "3 2021-12-01 17:44:00              NaN                                  NaN   \n",
       "4 2021-12-01 17:44:00              NaN                                  NaN   \n",
       "\n",
       "   Menhole cover status  TEMPERATURE  HUMIDITY  SURFACE   CO     CO2  CH4  \\\n",
       "0                   NaN          NaN       NaN      NaN  1.0  1131.0  0.0   \n",
       "1                   NaN          NaN       NaN      NaN  1.0  1131.0  0.0   \n",
       "2                   NaN          NaN       NaN      NaN  1.0  1131.0  0.0   \n",
       "3                   NaN          NaN       NaN      NaN  1.0  1131.0  0.0   \n",
       "4                   NaN          NaN       NaN      NaN  1.0  1131.0  0.0   \n",
       "\n",
       "     O2  H2S  \n",
       "0  19.8  0.0  \n",
       "1  19.8  0.0  \n",
       "2  19.8  0.0  \n",
       "3  19.8  0.0  \n",
       "4  19.8  0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('woojoo.csv',parse_dates = ['Datetime'])\n",
    "df.head()"
   ]
  },
  {
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   "execution_count": 6,
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       "        CO\n",
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    "df1=df[['CO']]\n",
    "df1"
   ]
  },
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       "        CO\n",
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    "df2=df1.dropna()\n",
    "df3=df2.reset_index()\n",
    "df4=df3[['CO']]\n",
    "df4"
   ]
  },
  {
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       "                      CO\n",
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       "1970-01-01 00:04:00  1.0\n",
       "...                  ...\n",
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    "df4.index = pd.to_datetime(df4.index, unit='m')\n",
    "df4"
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       "                      CO\n",
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       "...                  ...\n",
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       "\n",
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "df5=df4.resample('30S').ffill()\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "outputs": [
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       "[<matplotlib.lines.Line2D at 0x15a3a655ee0>]"
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df1['CO2'].interpolate(method='linear', inplace=True)\n",
    "plt.figure(figsize = (15, 6))\n",
    "plt.plot(df5.index, df5['CO'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15a3aa04fd0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GGAU1aWJnfPhkPdecwUGEMwAAAMcYhfzjYwE6OLowqAlBEsMagy0XKAbhLACWUzIAAKBEtXL4YIzpCkphDWuske2MaCCcAQAAOKYjFlPrgQ5fy7DWavveA1q3vTVt+brtrVq7rTXHqyqX+aPPiT7CjpiVtVZL392lto5OX8pu6+jUuu37JEn1dd2vOVvRsse3snPZ396pVVv26t2d+9XRGdCUlXBWQ9gVAAAAQLqYlX7zwju64cLjfSvjxr+8qbueW9Vt+RnfnyNJ+thJI/XjT07yvNzx35qV9rhXY70OdMS0cec+Tf/ps1qycZckafXM6Z6Wa63VhG8/nnzcs7Grj2JInx7JZefcOleHDuip5//9HE/LzmXN1lad9cM5acu8/tyIFnrOAAAAHDN5zKBkePBLajD70HHDuz3/yIL1vpZ/wcRD9OjV79OgPj3UEbMa1q9nMpj57bbLTtLowb0lScePHJD23Iad+wOpgyS9vXl3YGUhGghnPmH4MgB4a+LIAXrjex8KuxpAIE4cPTB5TVQQbr98cjKsBOUnl0zSpMMGSpKG9u2RnKjDL6lvP23CsEB/6DsXB6oAxxDOAACREeTBKhCmOmN8DyuZwvx6Bf15XdmV1NKsnCgO4QwAEBmuHFABnsiTRYwxigU8N0SYJz+MMfJ7LozUze3KvsSVesAdhDMAQGRwIINqk6vnpL5OwfecBVpa+mevrwv2p4dc6YV3YWgl3EI480nq/oXrzwDAG64cUAF+C2NYY5gj7IL+vK7sS9yoBVxCOAMARIYrB1SA34wxitlge5PC/HYZY9Tp80dN3Zau7EnYpSET4QwAEBl1HMigRtTHj9qD7DwL8+RHvQk4iDqyL2FCEGQinAUg6FEJAFCtuD4DtSJxIqJWZjAMYlhj+oQgbuxLOOGETA2VvNgYs1rSbkmdkjqstZO9qBQAAEAtq4sftXdaW9nBWgmC7sVJzUd1xqgzVoNnswlnyODF932atXaLB+8DAAAAHRxiGOTom6A7k1I/W12d5Hc2c3EkE8MakSmokzEAAAAoUmK4mx+9STv3teu2p5d3W+73UL+dre2atXhj8nHqMMZYTHp59TZfy39h5dai143FbLL30i+xmNXylj2+loHoqfSaMyvpb8aY+caYK72oUDWyTKYPAABK0NbR9YvMb2zY5fl7n/C9v+n2uSu7Ld+1r93zstLKveFv+vdHFiUf16eEn2zBLOZhMH1l9TZ95q6Xcz7/gWOGpz3+/uNLPSs7l9lvbtJ3Hl3cbXmH37/GDadVGs7OsNaeJOl8SVcbY87KXMEYc6UxZp4xZl5LS0uFxUUHgQwA/HHWUc1hVwHw3SljB0uS9rd3+lrOjPOP1ps3nCdJ6teza0DVSaMHSpI+cMwwX8q8YOIh+sNVp6uxPv9hqJcThCxevzN5/9Gr39ft+dsvP1m//cIUHTOivyTpL69v7LaO1xJh+NIpo3XFGeM0alAvSVJHLV57h6SKwpm1dn38v5sl/UHSqVnWucNaO9laO7m5mT+oAIDKTB4zKOwqAJ7Idwje1Nh1iOb3DIZfOvtw9epRL0nq36tRknTi6EE6fmR/367RmjxmsE4cnft7PLx/kyRvr0FLHR466bCB3Z6vrzM648ihOv7Q/vGy/Q9IiSKunnaEvv3hY/XPp41JW47aVHY4M8b0Mcb0S9yXdK6k7n2zAAAAyCrXZV6JCUGCnEo/McqwzsRnT/Sp7EKXcjXUeR9Mi712LzHUMoiZIxPbN3W7py5HbapkQpDhkv4Qv3i0QdLvrLWPe1KrKsD3CgAAlCvxI9SxAC8/SgSTujoT/90xf8opNNFGY733wbTYoYKJugURihNlJP6twwjkcE/Z4cxau1LSCR7WBQAAADrYoxZsz9nBkFBnJOtT2YXmQKxPBiTvyix2cpFEUAqi5yxRhMkIZ5b5QGpapROCAAAAwGNh9KIcDAk+/yh0gSn7ExOFeFl+sUMFgxzWmAiMDGtEKsKZT9L2O3zHAABACfzoPSpYpkn816iuzvgWDAv1nDXUJ36A2/trzgpd7xbkdk8Oa4yXWR/gkEq4i3DmE75XAACgXHUhDGtM9JyZ+LBG3645K9BzVp+cEMS7MhPXnNUXSGd+/vh3psxhjYZrziDCGQAAgHNMgNc+JcuM/7frmjPj6Y9Ap5VToPeq0YehhbEiw1kiGAYxtLD7sMbgJ4GBewhnAAAAjkmEiDA6UerrusqvpmGNiZ6zxDT9uSR+F9uvYJqq+7DG9OWoTYSzAPAVAwAApQhjWGOCMUbGx6n0C/WcNfgwrLHYa84SvVeB9JzZ9DIZ1giJcOYbl79W67a3avOu/WFXAwCAmranrUP72zuzPpc4YL/2odc9K2/ttlbd9vTyguslyl64dofe3LjLk7JTe8FMgb6zRM/Ztx9d7FnvWcueNknFDGs82GO5eP1OT8pO9fam3frTaxv08znLtTVeJ5MxrPGi257XztZ2z8tGNBDOatAZ35+jU29+MuxqAEBZThk7OOwqABVLHJjf99KarM/379koqWs43q+fXeVJmWf+YI5+8PhbOZ8fPqCnJGlInx56ZlmLJOn8//cPT8p+e/Oe5P3jRvbPu+767fskSX9/c5P+9NoGT8rfsKPrPS85ZXTe9d47fkjy/of/+1lPyk71ld+9qq/d/6p++MRbujP+75roKRzSt4ckqWV3m0644W+el41oIJwBACLlvYcP0dQJzTpiWN+wqwKUbU9bR97nB/RuTN5fuHaHp2XP+tqZeuDK07Ty5gvSln/vn47T3Gum6uLJozwtT5JaD3Qmyzju0AHdnl9+0/nJ++dPHJG8v7JlryflTxjeT5J03XkT8q43JSWc+aG1Pf3fvUdDXbK3btqEYb6WjWhoCLsCtYChwwBQmcyhTX168OcL0Rbk75dlOmZEv+T1Taka6+s0ZkgfX8pMXPM1ZkjvrM831GfvL/BqWGNnzGrEgJ5ZP3eQMmdi7NVYH05F4Cx6znzi5QxDAIDCkwgAURLmpA9hBBRri5vKPr7ywbselR+zhX9fLQiZ/+5FbQ/UFMIZACCSOAmGKCul/VZDW8+cmbBYXn10a60KzKIfiMxwRjZDJgeaaXVKPStlnZ67EQAiiAMaRFwpwxqr4SgiMayxmGxm0+57NKzRWid6zjozhjWGPcwS7iGc+aQaznIBAAB/1NpvWSWOi4oJSKmbxqvN5MqwxszjQ3rOkIlwFoBCv+cBAABqS+bEEHmFkOO8Dg2JnsJSr7Hy7poz60QQ6nbNmQOBEW4hnAEAAASso4R01p45Fi4AXg+360z2nBVeN3Uoo2c9ZzFXhjWmfyCGNSIT4SwAXHMGAABSZR6k5xPGtPte9+gkeoxKDSNeXSYSc+Sas8yP48IkJXALTQIAACBgpVxzFsZ17F6HhuRU+kUEpNTLQUoJsfnErFTnwLhGhjWiEMIZAABAwEoZqdgZQjjzvOcs/nmLmhAkZcSRV72G1plrztIfu9CbB7c0hF0BALVl9Za9uvyul/TMNdNkjNGctzZr7lstGtKnhwb37aHLpoyp6P1XtOzR/vZOHXfoAI9qDCBh7IzHtOLmC/jhXA/c8/zqotd9+q0W/esDr+rRhRskSY9cdbp61Nepd496jW/uW9R7xEpMOam9TGNnPKbmfk36/ZWnFV1epjXbWiUVOZV+SlXvem6V7npulerrjJbeeJ4a68vrV3hm2RZNOKRfya/71TMrtX7HPt1dwr9XKfJtj189s1Irt+zRw/PX6+LJo7RgzQ4dO6K/jj6kn+58dqU27WpLW//ik0fp4QXrNPeaaTpscO+s7/ngvLVa+u5u/frZVWnLv3j2eN0+d2Xy8cyPTdSMRxaV/8EkNdYb9e/ZqMumjFa/no0517tp1ps6bHAv/ePa91dUXrUgnAWgxmbLBfKa+qOnJUk/mb1M/+f0sfrc/7yS9nyl4eycW+dKklbPnF7R+8B97FrD8dX7F+i2y04OuxqR99iijZKk6RNH5Fzn6EP6aem7uyUpGcwk6WO3PZ+8X+y+bnnLnpLq9+3px+i6hw8enLfsbtP7b51b9r512aauzzGsX1PBdS+YOEK3Pb0ibVlnzOqnT76tb5w7oeSyN+/arwOdMe3e317ya2+a9WbJrylk1KBeWrd9nyRpzJA+ac9NHDlAi9bv7Fb2fS+tkSS9uXFXzvd9cP46SdKZP5iT89/pmodez7o8NZhJqjiYSVJ7p9XWvQf006eWF1x37bZ9emfr3m7boxYRznxCIAPyW7llr9o6gp+BDNWBfpvwLNmQ++AQpfv5ZSflfO7xfz1L+9s71RGzOv76Jyoq50B8f/uLPOWluuSU0frEyYfpjO8/pY0791dUtiQ11BsN6NWoYf175lwnNVCsuuUCbdrVptNueTK5bP2OfWWVfSA+hvTLUw8vav3VM6fLWqu9BzolqeJtn+nSKaN1xRnjdaAzpt6N9WnP/fmrZyT/zXOV/ep3PqgTb5ztaZ38NKRPD829dlrW57buadPZP3xakrS3rTPAWrmLcAYgFNZygA1EURjXP9WynhkH7+VKTERRyrDA+jqjpgZvpifojJVWtjFGfZrSP3u5vxubuN6tvoRZTowx6tvkz2FynTHq0VCnHjm2baF/8749o3X43lhfl3Nbth7oSN5ndvMuTAjik9QxxDQ1oLtOR35zBkBpOjr5qxZF5f4ItFestSrzcrGkcv9kJIJppeV7pdLJVqL2tzPvzKQpT3Hep4sjzRRArbGyZf+hBRCejjB+dAsVO/g7Y6W9zqsfSS7nd8Yyyy63JrHkD2C78Uen0mq48SmKl2+Pwd6kO8KZT0j/QH4xhjUCkVTqrH9wQ+LfreSA5FH5nbHwwlG5P4Dtl0q3gyMfo2j5fqfP0nPWDeEMQCisFekMiCB6zqLJhWGNpf6wdWZNyx/W2PVfV37wudJ/A1dCZrHyj2pM/U079i0S4SwQ+c4YALXKWlv2xd0AwkPPWTSVO6zRy/Ir7jEqd0KQ5LDGior3jCv1CEq+0JX6FJMNdSGc+YQZZ4D8YpZrzlAhdrOhaI/xExhRVO6wRq/OoXXa8IY1dsbcGtboSj2Cku98TupTnZz4kUQ4C0StfQmBYnDNGSrBfjU8HEBFU9jDGrt6zkp7TWZLK/drb0P+7JlcqUdQ8vecHXyOfUsXwhmAUHT1nNXWHyigGnDNWTSFPbTPljGs0avLQsL+7JlcqUdg8vWcpQ5rZN8iiXAWCK45A7qLWct3A4ggvrbR1FnmjIXezdZYejjLPFYv93xeZ7lDOn1Saycm806lTzjrJlo/MR4hmX+8Xl2zXVfft0DnHT9Cdz23Krl8aN8mbdnTppf/4xwN69cz4FoC/hs747Hk/aF9m5L3n1u+VSf/198rfv/Tbn5S7+7ar49OOlSD+xx8/7EzHtOqWy6ouT+CQBBWb9mrsUP7hF2NyNqyp82z97rhz0skSa0HOvTAK2u7PT+od6Pef/RwPbxgnaTSZyzMHIKXKC8h9ZimkH49SzzszDiWemTBevVq7P4e2/a26dGFG7otv3DSofrjwg0aM6S3JKnOkS6rIGaNzPx3ClPeYY0p/8g/e2q5Zi3amLUdS9Llp43RqeMGa8e+dm3YsU/XnXd03nIfeHmNGuvr9PGTR5VX8ZAQznyS+b276LbnJUm/f2VN2vLEDvrUm57U6pnTA6kbEBYvD0gS3t21X5L06MIN6teUvkub9852nTJ2sOdlArVu6o+e5m9WBeYs3SxJOqR/8SdlB/Zu1I7W9m7LH5zXdSC7u60j6+u2t7Yng5kkHTqwVylV1fUfOU6X3flSt/Kk0qc+370/ex1z6dNUn/a4rSOWVn7yfXN89j/GA9s7W1slSSMGlHcS/IozxunOZ4sPoYUcPqxv0eseNbyvlm3aow+/Z4T+8vpGfezEkUW9Ltt2Csv3/um4nM8NT/kOLFy7Qy+v3pZz3XtffEf3vvhO8vG/ffAoNdbnHgQ445FFau7XRDhDl1zTvc755lSdevOTAdcGqA2LvvehtJ66tnZmlQO8cMJhAzWwV6PmLmsJuypVITF865GrTi/6NQv/89y8z59842xt3Xug4Ps092squE6q9x0xNGcQ39F6QJNumF3S+5Wiob6uqJMAqfv9fBI9aKX69oeP1bc/fGxZr63U3/7v2cn7P7v04PJqOTnSs7E+7bMcf/0T2pMjbGcqdHKgzkiXTD6sovqFgWvOfJLac5bWdNzoUQcAACHxY+ZEfiOqMFd+hBq5lXLdWaEmH7PuDGUtBeEMQNXi7zAAF/nxg9BhTKZQ7o9Ch8WVCUGQm1cnGQ7+rp8nbxcowplPmIQACB/fwupGPwGi6uDU7t7tpeg4K4xDM/eVMotzvlX9+I4FhXDmk7SmYFOXR6+RAIBr2JMiyhJn9b0cZlfq5By1iBPn7iulA9jmOUUX9o+uV4Jw5hO+/4AD+B4CcFDiwNHLs/phDGvMd3AMlKOUdpxvVT+GDgeFcOYTesiA8PE9BOCi5IGjh0dhYXSc0VmHMOXrLWZYI7qJYFsAIqeUsekAKsB3zVOJA8eoD2ukVSBM+Zp8pw9Dh4NCOPNJalOg2x/wR6FjkQjukwFn8X3yjh/DGkMJZ4R2hChf+0t8x6K43yKc+SRXY4hiIwFcVehghK8bABclzup7eUwQwiVnnHpGqPK1ecuwRnRnUu5Fr2EAURDGwQhQyyJ4nOOkxIFjFGeSS8UMkQhTvp6z5LDGCH7HGsKuQNh+PHuZGuuMvnrOkZ6+79a9bcn7n7v7leT9fE3kqvvma9aid5OPr/nQBF097Yic61trNe7fZyUfL/ruuerXs1HfeXSx7n3xHUnSdecdrS9PPVx/X7JJV923QAc6Y8n1x854LO39Vs+cXvBzAalun7tCt/x1qSTpYyeN1I8/Oani9xw74zF98azx+rdzj1JTQ33O9f64cL2+/sDCvO91yR0vatUtFzB9skc6Y1aHf2tW2rKnvnG2xjf39aW8c259Wita9mZ9bs22Vq3asldfune+fvHPJ3n+b7yiZY9mL9mkL519eM51fvH0Cs1Zuln/+6X3elp2wtY9bTr5v/6u6RNH6OeXneRLGaVqqDNq7+w66Mn8G5LpijPG6ZRxg/Wh4w4p+v1jMasb/rJEdz+/OuvzP/30iWqoM3pl9Tb9z3PZ1/Faj4Y6LfruuWpqqNeSDbv00qqtOmZEf33qjhclSStvvkB1JR4A/nVx19/6KJ7VT0M2Q4iufeh1NTVm72dqa+863o1gNqPn7JdzV+jW2cs8f9//+MPirMsH9u6R8zVvb9qT9viHT7yVt4x12/elPf7VP1ZJUjKYSdL3H1+q3fvbdcVv5qUFs2x27W/P+zyQ6edzlifvP7JgfdpzpZytOnZE/7THtz+zUve/tCbvazKD2V2fnSxJuvmiiWnL39naWnQ9kN8bG3Z2W/bJ21/wrbxcwUySFq7dIUl6/I13tX7HvpzrleviX76gmX9dqtYDHTnX+f7jS/Xy6m2el51w3cOvS5IeW7TRtzJKdc/nTi163TufXaUv3ju/pPef9872nMFMkr52/6u66r4FgQUzSTrQEdPvX1krSbrgp//Q9/68JBnMJOnFVVtLfs+9bV3tyssDxz9cdXrBdb7xwaO8K1DSkL5NJa1//UeO9bT8hP/53Cm+vC+C9/CXC7fjhDXbWvX2pj1Zb2u2terYEf11wmED/ausT2q+5+xfzhyn2+euDKSsH118gurrTN4eqswzkdbanGeEM3tzEzv7buuVVk2gaPlGtJw2frDa2mN6KMuONrOdz/r6mdrb1qHjrn8iuSxxdr4Yqd+pS6eM1qVTRifL6GTYjWc6sowj3dvWGUJN0vnxT5wIZWEOS3dh22Y6/Yih+se103TmD+b48v6uDpM70JH75GY5vy9mJX100qGe9vieOHpQ4CNgCh3TBGXahGFO1AOVO3lM8O3YNTXfc1ZnTGB/DBrKOEVWStVy/YFw9G8dqoCX353M4T1ezXJK+/eXCwfTflTBgY8Vyo8Khy2CI5DKaisxa6M/pBGAL2o+nBljFLPBTAdbzkWJpdSqPdewxSLfxIWDEUSLl71Smccp3r01Ddsr2f5NXNi6fvxcSeIdwzx+bo/lH4oeFj//Vrh6fWi+z1zO5ojF3P2sAMJV8+Es8eN0QQSTcnrOSjkrnessa7Hvwe+VoFRentjPPHnh1XvXYOdDoFzYb4T9b+zXNoiF/cFy8LO31NW8ku8zl7M9YtaqvuaPwABkU/O7hsTxYBBDc8rpOSulXtmuB5GKP6vn6HEAHOblQSnDGqOg+8Z0Yb/hSziyifcupnzvi5fcvV7Sz1o5ms3yf2aGNQLwEOEsHpiCOMBoqA/rmrPi3sSFa0cQLV5+bzLPXXjVHP0Y8larsv2buLDf8KMGiXZTTPvxawt0pEyK40IPZYKfdXE1r+Qf1lhOzxnDGgFkRzgziXAWRM9Z6Zs7X7Uy9+s5w1mRZbk6hAbu8nLCAr8OVBy9bKdquJAZwp4QxK+/H6mjIVyaHMTfmrgZWPIFsLImBIkxrBFAdjW/awh0WGMZB59eXHNW7Fs49LcfEeD3mXyv3t+Fnp1q4e6W9K9mxTQfv9pY6j7dpf2zvxOC+Pfelcjbc8awRgAeIpyZ4IY1+j1bY0eOLoJih1xwEItS+P2d8ao5utTjAH/4eMlZUXtP3645SwtnLrVj/+oSxcBS1myNNpqfFYD/KgpnxpjzjDFvGWOWG2NmeFWpICWuOQviAK6xjGvOSpoQJNeP9hb5FhzEohTZ2ouXvWlevZOrkypEkaub0pdrzuIftpg2XWvhzNeeM//euiL52kE5+71YjJ4zANmVHc6MMfWSfi7pfEnHSvq0MeZYryoWlERnVhAXW9f5/CPUlc7W6NDffkRAtoNFL/O9V+2RaymrX9j7Lr8mnUkdDeFSM/Z1tkZH84rnv3NmbbdJkABAkhoqeO2pkpZba1dKkjHmAUkXSlriRcWCkjhzdfmvXy5rNsVSlPM7Z5f/+qWcwyH3HehMezx3WYsuuu25but9/u5XiirrS7+dr6bGmh/piiJlCz0f/8XzyYOr5Zv2aMIh/cp+/5/8fZmeXra57NcnfOm3C3TY4F4Vvw+k3fs7si7Ptt8J0pd/O18Dejd6+p6J5n3Znbn3wQmfvuPFsk6+FbJpV1vy/qW/erGsofFeeXvzHp06brCk0sNwKe1j294Dpb15QG6dvUxPvZV9f3TNg6/pl3NXlPR+re2dvrQZANFXSTgbKWltyuN1kqZkrmSMuVLSlZI0evToCorzx/uOGKJzjh6mA53eTul20uiBWrBmhyRpfHMfrWzZq+MOHVDwdf17NmhXygHQgF65Dzj6NjVo6bu7k4+njBusHg3dw9XgPj2KqvOQvsWtByRMm9Cs4f176oFXunYF/Xoe3KVMGj1Q0yeOyPq6MUN6652trZKk2y47Kes6k8cMUq8e9TnLbqgz6ohZ3XdFt91OmmNGlB8Qka5vU4OWb96Ttqy5X5P6NlXyp6R4//nhg4Mzfn7pSbr6dwskSSMHeR++h/Vr0ubdbXn3wQn9i1inHIc399GKlr2S8v8tCMLJYwbpohNHSpKOHNZXp44brJdXbSvqtaW0j75NDcl9g0sS+6OhfZu0ZU9b2t/qiaMGlDxE8awjm/WBY4b7UVUAEWfKHc5njPmEpPOstVfEH18uaYq19iu5XjN58mQ7b968ssoDAAAAgKgzxsy31k7O9lwlY9jWSzos5fGo+DIAAAAAQIkqCWevSDrSGDPOGNND0qck/cmbagEAAABAbSn7QgFrbYcx5iuSnpBUL+kua+0bntUMAAAAAGpIRVdxW2tnSZrlUV0AAAAAoGYxbzoAAAAAOIBwBgAAAAAOIJwBAAAAgAMIZwAAAADgAMIZAAAAADiAcAYAAAAADiCcAQAAAIADCGcAAAAA4ADCGQAAAAA4wFhrgyvMmBZJ7wRWYPGGStoSdiXgJNoGcqFtIBfaBnKhbSAX2kZtGWOtbc72RKDhzFXGmHnW2slh1wPuoW0gF9oGcqFtIBfaBnKhbSCBYY0AAAAA4ADCGQAAAAA4gHDW5Y6wKwBn0TaQC20DudA2kAttA7nQNiCJa84AAAAAwAn0nAEAAACAA5wMZ8aYu4wxm40xi1OWnWCMecEYs8gY82djTP/48suMMQtTbjFjzKT4cyfH119ujPmpMcbkKO88Y8xb8fVmpCz/SnyZNcYMzVPfccaYl+Lr/t4Y0yO+/CxjzAJjTIcx5hMebZ6aVkVt47PGmJaUul3h0SaqWVXUNsYYY540xrxujHnaGDPKo01UsyLYNrKuZ4y5MN4uFhpj5hljzvBg89S0Kmob16TUa7ExptMYM9iDTVSzItg27ou/fnG87o3x5UfH69xmjPmmR5sHfrLWOneTdJakkyQtTln2iqSz4/c/L+nGLK+bKGlFyuOXJZ0myUj6q6Tzs7ymXtIKSeMl9ZD0mqRj48+dKGmspNWShuap7/9K+lT8/i8lfTl+f6yk90j6jaRPhL1dq+FWRW3js5J+Fvb2rKZbFbWNByV9Jn7//ZLuDXvbRv0WwbaRdT1JfXXwcoT3SFoa9raN+q1a2kbGOh+R9FTY2zbqtwi2jQviZRhJ9+vg35Rhkk6RdJOkb4a9XbkVvjnZc2atfUbStozFR0l6Jn5/tqSPZ3nppyU9IEnGmBGS+ltrX7RdrfM3kj6a5TWnSlpurV1prT0Qf/2F8Xq8aq1dna+u8TMg75f0UHzRPYlyrLWrrbWvS4rlew8Ur1raBrxXRW3jWElPxe/PSbwvyheltpFvPWvtnnjZktRHEheNV6ha2kaWut1f6L2QXwTbxiwbp65AOCq+fLO19hVJ7YXeA25wMpzl8IYOHqRcLOmwLOtcooM7pJGS1qU8ty6+LNNISWuLWC+XIZJ2WGs7ynw9KhfVtvHx+BClh4wx2eqMykWxbbwm6WPx+xdJ6meMGVLCe6M4rraNvIwxFxljlkp6TF1n7uG9SLYNSTLG9JZ0nqSHvXxfJDnfNuLDGS+X9Hg5r0f4ohTOPi/pKmPMfEn9JB1IfdIYM0VSq7V2cbYXo6pFsW38WdJYa+171HX27Z6Q61Ototg2vinpbGPMq5LOlrReUme4VapKUWwbstb+wVp7tLrOvt8YcnWqVSTbRtxHJD1nrc3s8YE3otA2bpP0jLX2HyHWARVoCLsCxbLWLpV0riQZY46SND1jlU8pvRt/veJdunGjJK2P91D8Ob7sl+o6S31Y5nr56mKMeULScEnzJP2LpIHGmIb4WfCCr4e3otg2rLVbU152p6Qf5P+UKEdE28YGxXvOjDF9JX3cWrujiI+LErjaNqy1RU0OZK19xhgz3hgz1Fq7pZjXoDgRbxuZdYOHXG8bxpjrJTVL+mLxnwrO8etitkpv6rr4MfUizGHx/9apa8zu51Oeq1NXIx6f8R6ZF2FekKWcBkkrJY3TwYswj8tYZ7XyX4T5oNIv7L8q4/m7xYQgtI2UtiFpRMo6F0l6MeztWg23KmkbQyXVxe/fJOmGsLdrNdyi1DZyrSfpCB2cEOSkeB1N2Ns26rdqaBvxZQPUdY1Un7C3abXcotQ2JF0h6XlJvXI8/10xIUgkbqFXIGulus46bFTXxYvrJH1B0tclLYvfZqb+QZI0VVkObiVNlrRYXTPg/CzXHzF1zXCzLL7ef6Qs/1q8/A5JGyTdmeP14+NfvuXqOuBqii8/Jf76vZK2Snoj7G0b9VsVtY1b1DV2/TV1TfpwdNjbNuq3Kmobn5D0dvy970ws51ZTbSPrepKui+83Fkp6QdIZYW/bqN+qpW3En/uspAfC3qbVcotg2+iIv3Zh/Paf8eWHxF+/S9KO+P3+YW9fbrlviTNwAAAAAIAQRWlCEAAAAACoWoQzAAAAAHAA4QwAAAAAHEA4AwAAAAAHEM4AAAAAwAGEMwAAAABwAOEMAAAAABxAOAMAAAAAB/x/MSO4ZfA1NREAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df5['CO'].fillna(value=None, method='backfill', axis=None, limit=None, downcast=None,inplace=True)\n",
    "plt.figure(figsize = (15, 6))\n",
    "plt.plot(df5.index, df5['CO'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6=df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import array\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import ConvLSTM2D\n",
    "from keras.layers.normalization import BatchNormalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_sequence(sequence, n_steps):\n",
    "    X, y =list(),list()\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_steps\n",
    "        if end_ix >len(sequence)-1:\n",
    "            break\n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = int(len(df6)*0.8)\n",
    "train_data = df6.iloc[:train_size]\n",
    "test_data = df6.iloc[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler().fit(train_data)\n",
    "train_scaled = scaler.transform(train_data)\n",
    "test_scaled = scaler.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51964, 1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12991, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "col_train=train_scaled[:,0]\n",
    "# seq_train=col_train.values\n",
    "raw_seq_train=col_train.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51964"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(raw_seq_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "col_test=test_scaled[:,0]\n",
    "# seq_test=col_test.values\n",
    "raw_seq_test=col_test.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12991"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(raw_seq_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "n_steps = 30\n",
    "X_train, y_train = split_sequence(raw_seq_train, n_steps)\n",
    "X_test, y_test = split_sequence(raw_seq_test, n_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15a3aa4e2b0>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gspRNGDNvwCU9LAAAAACCQIALEUMo4QcBHwAAAH4R4LKU03XgGEQJAAAAIAAEuBDRAQc/CPgAAADwiwBXAAyRw0ioHwAAAPCLAJelrCYxseyfAwAAAACZEOBCZAyihA8EfAAAAPhFgMtSTpOYMEYOAAAAQAAIcGGiAw4+EPABAADgFwGuAGifYyRUDwAAAPhFgAsRHXAAAAAAgkSAC5EZEQ6jo4cWAAAAfhHgCoAGOgAAAIAgEOBCRP8b/CHhAwAAwB8CXAE4GugYAT20AAAA8IsAF6K+r8DRQAcAAAAQBAJciJjDBH6Q7wEAAOAXAa4AaKADAAAACAIBzi834Jcv5k1j4hhDiRFQPQAAAOAXAQ4oMgI+AAAA/CLA+WUDfvl7St8kJoEXBgAAAEApIsABRUbABwAAgF8EuAJghBwAAACAIFQUuwBxUeaNh6ws9z+I0rznfHXdNn3z9y8Ydf3DrR264MtPDFi2YsFk/fCPLlJ1ZXkWpUWUrH1pvx7Z2Kg3j7Xrxb3NOqV2vKorTm7P453dBSnHott+Kkk6bWaNystSdXPr/hbVVFdo/pRxMpM+8Y4luubs2QUpD5Ljy49u1bJZNfrLh14c8thf/85yffSSxVr70n79yfc3SpLOmz9ZX7nhHC2dWVPoogIAEHv0wPl06ZLpuuXyU/R37z3b93PmTxkrSfrZljd8rf+dp3cPWbbp9aM6eKzD999E9PzJ9zfqiZcP6MW9zZKkN5rbNWfy2P6fpTNqVFFm+of3nxvY3/zJJy6RJP30k5cOeexQa4fmTB6rmurU+Ztj7d2aM3mstr/Zql9tPxhYGVA6vv307mHDm3Ty+PfLV0/WrecbjuqlxuaClA0AgKShB86n8jLT5645I6vnzJ40NpC/zRDMZPnoWxfpL1edHurfOHf+ZO2581pJ0p47r+3vfZOkG1fO1/+++nRt2desa7/+tBZNG6dv31SnlX/7BHUNoRlct6hrAADkhh64EFk2U1YCAAAAwCgIcDHgmKcwUaIS7G3QRTHM6BVBeAYfx6hqAADkhgAXoqDa6TSqkyUq23NwkDQZJwsQmqFDKKlrAADkggAXpoj0tAAAAABIBgJchGQ6H815aoRhSA8cQygRosFVi6oGAEBuCHAhGvwdIwAAAADIBwEuQjL1fvBdkWSJ7CQmolcE4RlyGKOyAQCQE18BzsxWmdk2M9thZrcN8/inzWyrmb1oZk+a2cK0x3rM7HnvZ02QhY+6oBrqtHOSJSp5fOgQyogkSyRKxhNTHNkAAMjJqBfyNrNySXdLepekvZLWm9ka59zWtNU2Sapzzp0wsz+WdJekD3qPtTnnzgu22PGQbXOYBg2KLSrhEsnD8Q0AgGD46YFbKWmHc26Xc65T0n2SVqev4Jz7hXPuhHf3GUnzgi1micg4hLKwxUBpGO4EA41sBM0NueHdpaoBAJATPwFurqSGtPt7vWWZ3CzpsbT71WZWb2bPmNn1wz3BzG7x1qk/ePCgjyLFA0PSAJS8jEMoAQBALkYdQpkNM/s9SXWS3pa2eKFzrtHMTpH0czN7yTm3M/15zrl7JN0jSXV1dSX7fz3zGy/ZjySRopLrh7uMAFUNYaFqAQAQDD89cI2S5qfdn+ctG8DMrpR0u6TrnHMdfcudc43e712SnpK0Io/yxkpQ7XSGGiVLdLbnoFkoIxIskSyZhuVGZz8AACBe/AS49ZKWmtliM6uSdKOkAbNJmtkKSd9SKry9mbZ8ipmN8W5Pl3SJpPTJTxIt2wYxlwtAsVEDERaObwAABGPUIZTOuW4zu1XSOknlku51zm0xszsk1Tvn1kj6iqQJkh70vvf1unPuOklnSPqWmfUqFRbvHDR7JdJknm4bSRKVnq4hQyhlNLIRuL4qNfQycNQ1AABy4es7cM65tZLWDlr2hbTbV2Z43m8knZ1PAeNs8IWSgSgZXDujEiyRLJliGucKAADIja8LeaMwaOig2KhqCAvHMQAAgkGAC1NAPRoMNUqWqDZk6YBDGDINy43obgAAQOQR4EKU/SQm4ZQD8Is6iLBQtQAACAYBLkKYbrs0ROW7ZoMvNG9mNLIRuL46NaQnjgMbAAA5IcCFKCLtdMAX6ivCwOy6AAAEiwAXIRkbOrR0UCBcRgBhoWYBABAMAlyIBg9RyxWTmCRLVDLSkLBGFxxCwOy6AAAEiwAXItrDiBva1Ahchit509sLAEBuCHARknG6bdo5iRLZSUwkEhxCw0gCAACCQYALUVQa6oiWqATywScMghryC6TLOISyoKUAACA5CHAhsiwHUdKgQbHRS4KwROXEBQAAcUeAiwEaPskSlY6uYYdQAgFjdl0AAIJFgAtRtg11GjQoJjPqIILX16s75DreRSgLAABJQICLkEzD1xjWhkIhwCEsHMcAAAgGAS4GaFQnS1S255BJTBhEiRBkHkIZkR0BAICYIcCFiCGUiBt6SRAWjm8AAASDABcDtHuSJbKTmESkXEgWghsAAMEiwIWIywggbmhsI2hu0O/+5dQ1AAByQoCLEL4rgmKjpiEsHMYAAAgGAS5EQQ1Jo92TLFFpyA6ZxIQxlAhBphNQfN8SAIDcEOBClH1zmAYNiisq4RJJNLByUdcAAMgNAS4GaOgkS1Q6uoZMYlKkcgAAAMA/AlyIsh2SRlBDMaWqK5UQweo7rg0+vlHTAADIDQEuQjIHOJo6KAxOIiAsVC0AAIJBgAtRUEPSaFSjEKIytBPJkmmyEo5rAADkhgAXomwbxMzKhmIyGTUQgTs5hHLQJCbUNgAAckKAiwGaOQAAAAAkAlyomMQEcWLGReMRPDfod/9yqhoAADkhwEVIpvYMDR0UClUNQes7KcBxDACAYBDgYoBeERQCc5gAAABEX0WxC1Aq3v21X+l3zp2t3125QA1NbTp73iRJUkPTCT1Q36C5k8fqtzsPD/vcj353vdZ+6jItnj6+kEVGlp7ddVj3r2/QysVTi12U3Jhp39E2ffnRrXqxsVnf+r0LNGV8VbFLhYg41t6lrz2xXUtmTMjqec1t3brvude1v7ltwPLnG47qK+te0V9cdVrWw80BAChlBLgCeXl/i17e36K7frZNkrTnzmslSZfd9YtRn9vW1aN3fPWp/ucgmj54zzOSpEc2NY643qVLagtRnAGuPnOm1m05IEm6cFEqYE6bkApnH7hwviRpRs0YPb71qF490CpJWvGlx6lz6HfFP/y3Dh7r8LXu7EnVOnPOJD3x8gEdau3QbY+8NODxqeOr9PjWA3p86wGdWjtB7z1/XhhFBgAgkQhwIXv1y+/Wsr96bNT1vvsHF+r0WTUqN9OYynLJSWVl0j//fIfu+eWuApQUQfn8e5brmrNnDVjmnFRTXaFxVRUqLyt8b8M3PnyB2rt6ZCaNq0rt9hOrK7Xz765RX3G+8eHzdai1Qxf/n58XvHyIvr7wdvs1Z+g9584e8FjfKO+KMpOZafK4SlWUmXp6nQ62ngx908aPUUWZqa2rR2d+cZ0kqaFpYM8cAAAYGQEuZFUV/r5mWDthjGZPGjtk+aSxlUEXCSGbNr5q2G1ZTOVlpvFjhu7u6WGysrwscuVG9EzNon5XlNuw6w5XFwEAgD9MYhJxfDUkfthmSLKg6zcX9AYAIDsEuIgz5gYEAAAA4CHARQS9NgDiIPAeODrgAADICgEu4gh28cOU6EiyoEcFkN8AAMgOAS4iMjWKiAIAAAAA+hDgIoJOm+RgUyLJgjpW9b8OYygBAMgKAS7iCHYAAAAA+hDgIoKglhxsS2B0/R1wRS0FAADxQ4CLOC4jED9sMyRZ0JP0MIISAIDsEOCKxA1qtWScxIQsACCBmK0VAIDcEOCKZPBZZ9oyycG2RJIFVb1PDqGkCw4AgGwQ4AAARcMQSgAAskOAK5LBbRY6bZKDbYkkC/wyAgAAICu+ApyZrTKzbWa2w8xuG+bxT5vZVjN70cyeNLOFaY/dZGbbvZ+bgix8KeB7IvHDJkOSBT1JDx1wAABkZ9QAZ2blku6W9G5JyyV9yMyWD1ptk6Q659w5kh6SdJf33KmSvijpLZJWSvqimU0JrvjxNWQSkwxtIrIAgCRitlYAAHLjpwdupaQdzrldzrlOSfdJWp2+gnPuF865E97dZyTN825fLelx51yTc+6IpMclrQqm6PE29KwzjZnkYFsiuQLrYfZeh+/AAQCQHT8Bbq6khrT7e71lmdws6bEcn4tBGI4HIEqCPiQxCyUAANmpCPLFzOz3JNVJeluWz7tF0i2StGDBgiCLFFlcRgBAKeOQBwBAbvz0wDVKmp92f563bAAzu1LS7ZKuc851ZPNc59w9zrk651xdbW2t37KXBBo5cUSPApIr8NrN7gIAQFb8BLj1kpaa2WIzq5J0o6Q16SuY2QpJ31IqvL2Z9tA6SVeZ2RRv8pKrvGUlb/CwoUxBjVkoASQRhzYAAHIz6hBK51y3md2qVPAql3Svc26Lmd0hqd45t0bSVyRNkPSgFzhed85d55xrMrMvKRUCJekO51xTKO8kZoYOoaQ1kxxsSyRXcHOYpF6JDjgAALLj6ztwzrm1ktYOWvaFtNtXjvDceyXdm2sBSx25DkCSDb6kCgAAGJmvC3kjfOQ0AKWEk1MAAOSGABdxtHHiiB4FJFfQtZsOOAAAskOAKxLflxHgNDWABOo7spHfAADIDgGuSIbOQklQSw62JZIrsElMODkFAEBOCHARRxMHQJIxhBIAgOwQ4IrE9xBKAEggDnkAAOSGABdxBLt4YCp0IDeDh5MDAICREeCKxG+The/GxQP5DciSd2hj3wEAIDsEuCIZ3GNDTxuAUsIhDwCA3BDgIo5gFw9uhHtAklC7AQAoLgJckQxuBDGlNoBSwjEPAIDcEOCAAAwcEkvDFMkVdO1mAiAAALJDgCuSIZcRKE4xEBCaoEB2+jrg2HcAAMgOAa5YaLUAAAAAyBIBLiL4Oki8DexRJZ0juYKq3X2HPEZQAgCQHQJckQy+eC3Xe4s3LkYMZKdvEhP2HQAAskOAAwJHGEdyUbsBACguAlyRDJnEhFZRrDEMDMgOQygBAMgNAa5IaLMAAAAAyBYBLiLogANQSriMAAAAuSHAFcmQi9eS4GKNYWBAtrxJTNh3AADICgEOAAAAAGKCAFckg086cxmBeGMqdCA7JyduYt8BACAbBDgAAAAAiImKYhegVNV9+YkB98vLhu+Bq6k+uYmWfG6tuntTZ6unjq9S0/FOfePD5+uas2eHV1CMqKfX6dTPrR2wbPyY8iKVJniLbvvpsMtf/OurNLG6ssClQa7e/83f6IWG5oyPd/b0SpL+8NLF+qv3LB/y+DO7DvffHjcmmH8bcyZV6+CxDv3wuQY9vKFxQDkkacmMCZo/Zay++wcrA/l7AAAkBT1wBfDon17af/sDdfN01tyJAx4/b/5kTR1fNexzrzlrtiZWV+jqM2fqnWfM6F/edLxTkvT1J7eHUGL41dHd03/7tJk1WjZzgi5dMr2IJcrfV244R5L0/gvmZVxn55uthSoOArBlX4vOmDNRN1+2eNifPt9+evewz9918LgkadnMCbp8aTD1+z/+8C06fVaN/vjtpw4phyTteLNVv9h2MJC/BQBAktADVwBnzZ2kPXdeO2BZes/GP33wvIzPLSszvfjXV0uSfvHKm1q35UAoZURu0mfQW/dnlxevIAF6f918vb9uviTpwQ17i1waBME56S2Lp+ozq04f9vF/eWrnyM/3vqf2Hze/RWbBfF93YnWlfva/Bu4zo5UDAADQAxcJAbWHACBUHKoAACg+AhyQB+bPQxw4ubzCF9dqAwAgOghwEcAlBOJryAXZgQhyTnl1n/XXcg5VAAAUHQEuAhhCCSAOONkEAEDxEeCAPND/hjhIdcDl0wVHTQcAICoIcEAeSrVdW6Jvu+QxWgAAgOIjwAFA0rn8wheBHQCA6CDARQBntWOsRFu2VNl4CWoWSrY7AADFR4CLgKAujAsAYeJYBQBA8RHggDy4Uu2CQ6y4fIdQluqXPQEAiCACXARwTju+aNciDoKqphyrAAAoPgJcBDAqCUDY8rmMQF8A5FgFAEDxEeCAPNABhzhwzuU5hDK4sgAAgPwQ4CIgrwvsAkVAe740cawCAKD4CHBAHpjcAXHglN/316jlAABEBwEuAvheSXyVasOWKhsvgZ1nYMMDAFB0BLgIoE0EIHR5nCnq62nmZBMAAMVHgAPywAhKxAXZCwCAZCDARQEtq9gq1Qt5l+a7BocqAACKjwAHAAkWxPBHepoBAIgOAlwEMDV3jJVow5YaGx9Bhi/jS3AAABSdrwBnZqvMbJuZ7TCz24Z5/HIz22hm3WZ2w6DHeszsee9nTVAFTxK/baJSHa4HIH/5nCjqO/YQ3wAAKL6K0VYws3JJd0t6l6S9ktab2Rrn3Na01V6X9FFJfzHMS7Q5587Lv6hA9BCpEXVB1FGGUAIAEB2jBjhJKyXtcM7tkiQzu0/Sakn9Ac45t8d7rDeEMiae37PaDLWMnlJt2Jbo2461IEY/MoISAIDi8zOEcq6khrT7e71lflWbWb2ZPWNm12dTOAzEEEoA2eqfxCSf1wimKAAAIAB+euDytdA512hmp0j6uZm95Jzbmb6Cmd0i6RZJWrBgQQGKFC1MDBBfpRqqqbHxEWQNZRQAAADF56cHrlHS/LT787xlvjjnGr3fuyQ9JWnFMOvc45yrc87V1dbW+n3pxKBJFF8MoURcBHEZAc41AQBQfH4C3HpJS81ssZlVSbpRkq/ZJM1sipmN8W5Pl3SJ0r47BwAIVxAnGUq1pxkAgCgaNcA557ol3SppnaSXJT3gnNtiZneY2XWSZGYXmtleSe+X9C0z2+I9/QxJ9Wb2gqRfSLpz0OyVkP+z2gxfih6atYgLhmoDAJAMvr4D55xbK2ntoGVfSLu9XqmhlYOf9xtJZ+dZRng4Cw4gW0EcN0p1qDAAAFHk60LeCBc9a/HlSrRlS42NjyCrKJ14AAAUHwEuCmgUxVaJ5jf6gmMokOvAcbACAKDoCHAAAAAAEBMEuAhgWBKAsOXTe9Z/MXCOVQAAFB0BDgASLIhruJXqUGEAAKKIABcBnNSOr1Jt2FJn4yPI2WvZ7gAAFB8BLgK4PlN8leqlHUrzXcdbPkeZvu3NsQoAgOIjwAFAgpVqLzEAAElFgIsAzmnHV6k2jqmz8XGy9yyP1+j7Hl3epQEAAPkiwEUAo5Liq0TzW8m+7zjLaxZKMQslAABRQYADgARzpdpNDABAQhHgIiCfM+MoLhrHiItAhlDSBQcAQNER4AAgwTjFAABAshDgIoCT2vFF4xhRF0QnMfUcAIDoIMABeSjVEZSl+r7jLK/hj85xogkAgIggwAFAkhG2AQBIlIpiFwD5DaF85Y1j+tKjWwcse/LlA9rX3K7xVeVaOqNGz+1pkiSNrSxXW1ePfvrJS/WfL+zXwmnj9KGVC/Ipesna3NisH21q1NETXcUuSlF899e7tfal/Vk/b9fBVh0+3qkLF03VkeOdWv9ak648Y6a+++s9kqS5k8eq8WibJGlGzRidMXuiGo+2ac7ksWpu69ILDUd19ZkztW7LAU2srlBLe7ckafK4Sh090aULFk6RSap/7YhuvnSxvvP0bknSNWfP0tqX3ugvx4yaMXrzWIckqW7hFC2bVaMfPPu6Ll9Wq1++erB/vbPmTtTmxhbdfOni/mX3r29Qa0d3//1F08ZpXFWFtu5v0bKZE/TqgdYRP4Orls/Uf209IEk6fVaNXnnjmKrKy9TZ0ytJ+vS7lumRjXt1wcKp+uX2g2rr7NEn37lEh1o71drRrZsvXazf7jysJ18+oHecPkM3XDBPdz72ir7329d07Tmz9YG6+Wpt79a158yWJP1ye+r9+D3MDD6eSNKG144Ubaql4crjR0+v0yMb92rSuErVLZyqqeOrhl3vjeZ2vfJGi95+2oz+Zc/uPqwZNdVaPH18Vn+zr75dsmSaTp81UW8e69B/vrBPcyZVa19z+7DPKTNp4+ffpXd89Sl1dPfqRGePHvifF+sTP9iog8c69D9WzNVlS6fr0iXTNWNitSTp4Q17ddmy6XqxoVkP1Dfoi9edqZ+/8qZ+7y0LIjnRzObGZr3edEL/9ps9+vqNKzSjZoz+6Hv1uvWKJVqxYIp6ep1O/dzaYZ87f+pYNTS1DVj2tmW1cpLed/5c/fLVQ3p4415dtnS6fuecOfrAhfP71/vlqwc1dXyVmo53atqEKh1q7dTxjm6VmXTf+ga9bVmtZk6s1onOHpWXSdsPtOrIiS6947Ra3fvr3Xrv+fN0oqNbv3/xIpWXnfxcP//jzVq5eKretXym/umJ7erp7VVXj9PL+1v07x9bqdt/tFnP7j6sO1afqY/9W70kafqEMbrm7Fn60cZG3XL5KfqnJ7erp9f1H2Puet85et8F83T7j17S/KnjtOG1I/rLVaepqrxMV/zDf+vKM2bo3HmTtfvQcW07cEzfvqlOsyeN1euHT+jyr/xCknTXDefoA3Xz9ctXD+r5hqNaMmOCnJMOtXbo3WfP0m0Pv6QlMybox5sa1dzWpRsumKdz503WPzy+TVUVZVowdZye2dWk1efN0SMbG/vf7+mzavTBC+fr7l/s1KHWjiHbaN6Usdp75OQ2evtptTpyvFN1i6ZKktZteUMrF0/VlsYWzZxU3X+M/ehbT36u33l6txZPH68rTp+h+557XWVmA7Zl1L28v0W/2Xm4/3/L7120QF++/uxiFwsJZlGbRa+urs7V19cXuxih+/h/bNDPtqQalLv+7hqVlY3+T7fpeKfO/9LjQ5ZPGHMyh/c6pxOdPb7LsefOa32vi5P+4sEX9NCGvZowpkKtHd06a+5EPfqnlxW7WIH75A83ac0L+4YsT69z2egLPn2fW1xUV5apoqxMzjkdz2L/KoTrz5ujHz8/dBv17dvX3/1rPd9wVP/fzW/RpUunD/saH/72M/r1jsOSMm/bZTMn6JE/uSSgUg9v0W0/HbIs37o22uv0rTe+qrw//KTXU78GH3uDruNnzJ6oxz51mQ61dqjuy0/o7LmT9FJj84B1fvhHF+niU6cF9jeDMni7/t8Pnqs/u/8FSal6+tCGvfqLB18I5G/99/9+uxZOGz/s383VXe87pz9MOOe0+LOpsPnHbz9V//LUzgHrfuTihfreb1/L6e/8/fvO1mcefsnXutefN0f/dOOKIe9xz53XBva+8zVhTMWobZIJYyrU3dur9q7UCazxVeX9x9j0fTLqhtvXf/vZKzR70tgilAZJYWYbnHN1wz1GD1yRfPP3L8j6OVPHV40auN5sadfKv3sy12LBp95ep3lTxurpz1xR7KKE6usfWqGvf2hFYK/X17DY/DdXa9ntj6mzpzdjAMnXJUum9YeS0Zy/YLI2vn404+Pf/8O36IKFU9XW2aMzvvCzgEoYjMPHO0dd57Kl0zOGN0n6/h9eFGSRchbkCaXPPPSi7q9vkCRVlps2/83Vw67XVyef+dw7VVNdOWBZpucMp/Fomy658+f99zf/zdWBNqQPHkv14HX3pE66vnlsaI9ee3e0Ti5k0toxsJyt7cGNZOjyerKDlN44Tz/nPdwIjCYf+2Mmx9r9B/6+kQpRtvlvrtaeQ8f19q8+NWCUQZ+PXLxQd6w+S5teP6L/8Y3fSJKeu/1KnfnFdZKkDZ9/l6orywte7lwMt6/37atAGPgOHICiCusMK9dXPCkuZ7EBJBSHICBQBLik4SBZEE5c/iHqstk+fgNOFLf5aKPgOQfsT74hN4JVAyFgf8qOn90qfd+L4jEWiCICHICiisL/6yiUIUxJf38AAJQSAlzCMGysMJxzfNb5skG/A1aWxanc0VeN7rZ2o/UJRGyiqkJJ36Z+9tV8tzA9B6UhahO/RV3fvjfcPG3D/QtI31fZp4DMCHAJwwGvMBhCGYCQ20HBbh8XwmsGw88QyiiWO2zpn8uoIVf5f0ac0CkNxLfsjLRfDfdZZnviBShVBDgARRXWP2n+9Z/EZwGgmAhjQLAIcAnDIbIwnOOzjrpsJqXw27iIYiNk1B44ugx8yXfblmIvZylifyoc9ikgMwIcgKIK6590Vi8b44aCv+GBMX6DGGDE7R2XcDEoBcWl2EMNLXl83wuAOCHAJQwNtcJIfa+IzzoIYX2KwW4eC+E1C8NPwEuirCcxyfs7cCgFpbo/5apvvxpu/xpunxm43wLIhAAH5CA1CyWiLcgt5AJ/xaD4GUIZxXKHLdtJTOJixDAalw09qDUfp2GJA8s69AOPyyYolL4TncN9LsNOYjJgFko+TSATAlzCcLhD3IQ2hJKdoR+fRQHwGQMACoQAlzA01ArDSTTYAhKPWSj7hlBGb6Nn6rzou15VnHo3glT4IZTRqxtxUaJVtCT0X+ttmB2MIZRA7ghwQI7451J6IrnNfbV+I1ly5CCRk5jE9iwDk5iMJtsTIwMu6s1hC8iIAJcwnAUuEP5LB4YhlOHoaxNTVf3hMgLwI7ZZE0CiEOCAHDi5SA6nQ1i8SUwiuMkz9cj0LXXORbLcYWMSkwiLcYUcWJeYxGQ0I9XXYScxSasb/I8FMiPAJQ3HO8RMeNeBY2fowycxOi4jAACICwJcwnDCqjBKdWr2cIQ0iUko14GL3lbPNKQrvt8rCkbWk5jk/feiVzfiIk5VNepljdp+P9JuMewkJqGVBEgWAhyQI9prpSRajaJslWJdTeoQyiRi+yTXSIee4YdQhlUSIFkIcAnDsa8wInaSEyUs42UE+n47hpP6kW8PGp9waYj6oT+q/5ui1jMIxB0BDsgRjeJgMAslUNpo2ydYtpcR4MAN+EKASxgOfoXBkJ/ghFVjSyVgZzqzffIyAqU5C2W28v8OXCDFGFUirwNXpGLkYuD+Fr2SR69EKbRNgGAR4BKGQ2RhOEcPT1BC+xxDmMQkijIPoUw9Uqq9G1lPYpL3LJTRrSMIzmi7U7F3t6gNVRxpv2CPAXJHgAOAUUWrUZStUjzZkNRJTEZsn8dlOw+qkBHLHFngOnCjGenYE9vNDkQAAS5hSrGhVgz840FUZL6MgPdb9A75kfcQLz7ikhD1sBnV4kWtZxCIOwIckCPG9AcjrHAR7KuyreMm2yGUcZHEZnCSekgx0Eh7XnL2SqDwCHAJk6SGSpRxIe/gkIPzM+p3cqisBVGwSUxG6smISw4aPIlJXMotDfqMo1fwqH2WfSc6OeEJBIsAlzAcIwslYv8lY4wqm6dRZ6FEIVCPS8NovYXF3t+i2ptZikMoS/Ato4AIcECOCMvRFuz/znj/Jy7FqsokJhGWmIMnk5iMZqTPIzl7JVB4BDggB5xZQ1SMdhkBOYYvFQKfcWmI+rE/6uUrJUk6cYTo8RXgzGyVmW0zsx1mdtswj19uZhvNrNvMbhj02E1mtt37uSmoggPFRnstGGE1fJnEpLQldRKTJCrF4XWlYqTDO3slkLtRA5yZlUu6W9K7JS2X9CEzWz5otdclfVTSDwY9d6qkL0p6i6SVkr5oZlPyLzYyIVQUBlOzIyr8XUYAYSvUZzxi1olLDorxJCZuhHt5v3aMPge/+v5PlmIPdRK3J6LDTw/cSkk7nHO7nHOdku6TtDp9BefcHufci5J6Bz33akmPO+eanHNHJD0uaVUA5UYGhIrC4IxxcErw/3qgMg3T6VtKXS0M6nFpGG1/ymdvC2LIXVR391I8DpXeO0Yh+QlwcyU1pN3f6y3zI5/nApFGgy3amMTkpFKsq4mdxGSk9xKX7TyoQsZ36zCJyahG+EDiu92B4ovEJCZmdouZ1ZtZ/cGDB4tdnFgrxYZaMfCPB1GReQhl6gGGUBZGoUY/MIQyq5cOXNSHUCbpZEXclWKvIwrHT4BrlDQ/7f48b5kfvp7rnLvHOVfnnKurra31+dJAcdEoDkZYDd9SmcSENsLwmMQEcZPEXZlJTIBw+Alw6yUtNbPFZlYl6UZJa3y+/jpJV5nZFG/ykqu8ZQgJB8TCcE50dwaEjzEcJ78DV5oTCBRaoT7iJDbyg+w1CvvzifoJk6iVr5SPPBHbFEiYUQOcc65b0q1KBa+XJT3gnNtiZneY2XWSZGYXmtleSe+X9C0z2+I9t0nSl5QKgesl3eEtQ0hoqBUGB+bgUGPzk/E6cP2zUFJbUTrCH0IZ4iQmgQyhRFRELUwjWSr8rOScWytp7aBlX0i7vV6p4ZHDPfdeSffmUUYgkggeiAvqanKM+L2auGzowZOYxLahyyQmo+GkMhCOSExiguBwqCwM5xxD/wLC55ifjA36vh44ZjEpiEgMoYxLEBo8iUmQLx32h+Ay3gngpYO4jEBcKkEpYFsgPAQ4AACAIiN7AfCLAJcw9GYUDh91MBhiE46+s/nOMQtjIUTiMgJxFeCbKuxlBKIn6uUrJYncVxEZBLiEoTFcGByYg0ONzU9vhspIHS2swh16k7dhAx1CGXaAG+X18/n7QQx/ZL+PDjYFwkSAA3JEWEZcUFUBAEgOAhyQAydHzxEi7eR14KirhVCozziJPSxBvqewJzGJ/GU5Il68UpLEfRXRQYADgBijkQBERz4Bj30ZgF8EOCAHzjEsDdHW930aJ+pqIYQ/pDr1+q7/3jB/Ly7befB14ALsNgojBKUXd+DrF+8DzzRpTuR7CEsI2wJh8nUhbyTXa4ePF7sIQ3T3OpWb9f/T7Ol1qqooU3tXj8ZUlKuzp1fOSTXVFers7tXEsZU61t6lSWMrVWaWWq+yXOVm6nFOHV09mji2UuVm6vZeq+91e51TZXnqfm+vU3t3j6orylVWZmrt6Na4ytRtKdUg7uzpVUtbt9q6elRZxvkPFF9rR/ewyxuOtKm1o1vdvY5ZKBOgpb1Lrx0+rsajbf33BzvQ3B7JY/pgB4919N9+7fDxAffztb+5XTXVwTZtDh7r6P9cD7ScLGvT8aHlPtY+/P7ox6FW/59Dc1vXsNu6oakt57+PYO072qaxleVZP6+792TwqyhLtVt6e50qystUUWbqdS7VlikvU3mZqafXacKYCvU4p4oyU3mZqcxMzW1dqig3TRpbqY7uXlWUmUyptlVnT++A51d6t9N19fSqzGzI8j59bagyswFtqThxzqnxaJt6ep1mTxrb3z6MAwJciXvbV54qdhEK5n3nz9PDG/dqz53XSpKuv/vXeqmxuf/+nz3wvH7y/D6tPm+O3nv+PN1073M6d94k/eTWSyVJ//eJ7fr6k9v7X+/SJdML/yYS5IKFU/TbXYe1ePr4UF7/jNkT9eiL+32tu3z2RD23uynj45PHVQZVrMDtb24fdvn1d/+6/3ac/ikFZcmMCf23z184OeN6i6aN057DJ4Ysz7ZDLeyI3NndO+B4faKzZ8g6tz3yUsilCEb6cTTo/0F/9L36QF9Pkr7x1E5946mdQ5av23JgyLJfbT+U89/599++5nvdbQeODfvZ/c7/ezrnvx+GCq/xf/7CKUM+m75jf5SPr/n42L8FXxfDcuGiKXrw428dsGzp7Y/polOm6r5bLh72Ob/zz09r6/4WnTF7ol7e39LfloqTBzfs1V8+9KIk6YlPv23A/42oI8Al0NpPXqajJzo1prJMZqbvPL1bZ82ZpP3NbXr0xf3686uWafuBVk0cW6lF08YVu7gDHO/o1ud/sqX//l9ctUxf/a9XA3nthzfuHXD/pcbmAfd/8vy+/t9TxlVJkl7Ye3KdB+sb+m/PmVStL11/ViDlKiXPfe6d/T1G/3pTnfYcOq4z50zU1PFVOnqiS7MmVevZ3Yd14aKpmjyuSrMnVevoiVTv6vHObv385Te16qxZun99gy5fVquDxzp08Fi7Fk4fr2d3NenSJdM1c+IYrd9zRB9aOV8nOrtVZqYPrVyg53Y3qafXqf61Jr33/Hl6eX+L5k8ZpyUzJmjmxGotnz1RKxdP1U+e36eZE8eo/rXUa+w72q5Ta08e1O+/5SLtOnRcleVlenHvUX3wwvmSpM/9aLP+4K2L9Njm/Wpp69aRE51qPNqmCxdNlSTNmlStvUfadMVptfrRpkYdau3UabNqtPNgq86dN1n7jrappb1LV5w+Uw1NJzR1fJW2HTimBVPH6fRZNdp58Ljau3p0/oLJ6upx2nGwVWMry7V89kRtO3BM6/c0acHUcTpzzkS1tHXrlNpU48hMuqQETzZ87JLFOmfeZI2rKteiEU4S/PgTlwzoVZGkJ//8bVn34pSVmb71+xdow2tH9AeXLJIkbfr8u3R/fYMuWDhFW/e1qKa6Qj/b/IbOnjtJm/c165TaCTrW3qUV86foB8+9rprqCh1r79YNF8zTr7YfVHNbl5bOqNGYyjItnja+P4hvO3BMp0wfr2Pt3dr2xjFduGiqXj1wTMvnTMzuQyqQIye61NrerU0NR3T1mbM0pqJM67a8obcsntbfeL9/fYOuOXu2XnmjRc/salJ1ZbnKy6RVZ87SrkPHtf1Aq7p6ejV1fJXOmD1RDU0nVLdoit5o7tCD9Q26+qxZmja+SqfNqun/u41H2jRuTIVOdHT3/zaTOrp7tevQcZ06fbwqyst0vLNbJlNPb9/ojkpt3d+s02dN1LH2bp01d+Dnun7PEc2dXK3Zk8ZqU8MR9fSmRoQcPdGpi06Zps2NLTrQ0q4VCybrP1/Yp33N7Vo+e6KWz5mozY3NOrV2gl7e36KxVeVaNrNGm14/ootPmaZF08frNzsPa+bEMXqzpUPL50zUxOpKPbihQbU11Zo2vkod3b3q6O7RJadOl1kqyD+yca/mTx2nZTNrNHtStRqPtOlQa4dqa8aoq8eptaNby2ZO0BMvv6nzF0xRw5ETev3wCZ01d5LKTNq6v0Xjx1SooszU0dWrhdPGafO+ZlWUlfW/zrnzJuvVA8fU2tGtmuoKdfc4VVeW67RZNXrbslo9vvWA3jzWoYtOmaqLT5mm494JhurKcj36p5dq8fTx2ne0TZXlZdr4+hH19DrdcME8SdLCaeP1rx+p07wpYyVJ991ykcpiNu77N7ddoR9tatSBlnbV7zmiFQsm64KFU7J+nfV7mvTD5xpGXzFg6/ccGXb5M7syn9jcur9FkvSy9zuODrd2SpLufO/ZmjFxTJFLkx0L4rojQaqrq3P19fE5a4FgHTneqRVferz//mOfukzv/tqvAv0bfWeJFt3202HvS9JH37pI//abPQMev/j/PNnf23HV8pm65yN1gZYLAACUrv98YZ/+9Ieb+u/XjKnQsQzD5IM2uAdtcBtpsPQ200jrRdk3ntqhu362Ta98aZWqcxjuGjYz2+CcG7axWXrjahBpMTvpBgAAEIhodakkX8T6sLJCgEOkRTXQRbVcAAAgIWhrIAMCHCJl8Gx5UZ09L6rlAgAAgH9xPClPgAMAAAAiJoa5AgVCgEO0DDpaRfWsSFTLBQAA4mlw08JobISqbyLHOI6qIsAhUjhWAQCAUhTjOTViqW8Skzi2PQlwiLSo7lNx3NkBAEB80NZAJgQ4RMrQ4QNFKcao4tjdDgAAgJS+Hs84tugIcIBPcb5eCAAAiJc4Bos4iXO7jgCHSBn6hd3iHL7caHs1R1UAABAgJjEpjjh+zgQ4REqUd6EY7t8AACAmYtwhFEtOfbNQxg8BDpEWRmgatXdNox9E47izAwCA+KCtgUwIcIiUwYEtjIOXnzHPo46gpDsOAAAEKC4TuSUFlxEAAhLl2R3j/GVXAAAAnNQ/C2UMExwBDpEWxk7lJ4e5UdaK364OAADihdYGhkeAQ6RE+SRIlMsGAADijYE+BRbjoVUEOERaON+B8zGJyajfgQuoMAAAAMOgrYFMCHCItFBmoQxgHY6pAAAgSNG4Em7pcIpvSCbAIVKivCPFuKcdAAAAaZyLb0gmwCHSwpiVkssIAACAqKOpgUwIcIiUKF9GgAMpAAAICwN9CsvJxfaEPAEOkRbOd+By+xZceq9cPHd3AAAQF1E+qZ0Ecf5qDAEOkVKIEyFBDKHkmAoAAII0ZBIT2hqhi+tHTIBDpER5R+JACgAAwhLjDqFYYhZKICTF2rFGncQk0lETAAAASUWAQ6TE9cukAAAA+aAFVFipywjE81MnwCFSho7/LtJlBEabxCSe+zsAAADktfVi2p4jwCHSwtiv/MxCOfoQSgAAAKDwCHCIlCj3bEW5bAAAIN6YxKTA4tsBR4BDtIVyHThfQyhHRpgDAABAMRDgEClMYgIAAEoRLaDC4jICQEjCmB3IzxCF4XrpBkxiwmEWAAAgtpxzsW3PEeAQaeEMofQxickoMS+uZ2wAAAAQbwQ4wCdCGwAACAuTmBSWc/Ft2xHgEGnhXEYgt5W4DhwAAEAyxDkwE+AAAACAIuPccOHF9TP3FeDMbJWZbTOzHWZ22zCPjzGz+73HnzWzRd7yRWbWZmbPez/fDLj8SLqIXkYgvrs8AAAAUkMo49meqxhtBTMrl3S3pHdJ2itpvZmtcc5tTVvtZklHnHNLzOxGSX8v6YPeYzudc+cFW2yUilBmB/IT4EZJeTHd3wEAABBzfnrgVkra4Zzb5ZzrlHSfpNWD1lkt6d+92w9JeqfFNdICGVCjAQAAksHJxXY8lZ8AN1dSQ9r9vd6yYddxznVLapY0zXtssZltMrP/NrPL8iwvSkwolxHw0QU33BoDrwMHAACAuHJOsW3QjTqEMk/7JS1wzh02swsk/djMznTOtaSvZGa3SLpFkhYsWBBykQAAAAAgnvz0wDVKmp92f563bNh1zKxC0iRJh51zHc65w5LknNsgaaekZYP/gHPuHudcnXOurra2Nvt3gcQK5TICvr4DN/LjDKcEAACIt7g25/wEuPWSlprZYjOrknSjpDWD1lkj6Sbv9g2Sfu6cc2ZW602CIjM7RdJSSbuCKTqQGz/X/YjztUEAAACQXKMOoXTOdZvZrZLWSSqXdK9zbouZ3SGp3jm3RtJ3JP2Hme2Q1KRUyJOkyyXdYWZdknolfdw51xTGG0EyRWkunPSihDI7JgAAAArCORepdmY2fH0Hzjm3VtLaQcu+kHa7XdL7h3new5IezrOMKGHhDKH0MYnJMOsMmMQknvs7AAAAlBptFdf2nK8LeQMAAAAAii/sWSiBvIRxZuQTP9ioqory/vsfufe5Ies8+uL+IY+/0dLev6yijHMfAAAgGYZrC420PNf1omTHgWOx/UIMAQ6R89G3LtKYijI1Hm1TTXWlrjt3jta8sC/v1x1TUaaO7l61d6V++rS0dUmSqsrL1NnTq6ryMi2ZMUFb97eoZkxF/+PTJ1TpUGunJOn3L16Yd3kAAAD6XHnGTF15xoz+tsaXVp+lr/98ux7feiD0v93X1vG7PNf1omTGxGqdv2BKsYuRE/PzfaBCqqurc/X19cUuBgAAAAAUhZltcM7VDfcY48AAAAAAICYIcAAAAAAQEwQ4AAAAAIgJAhwAAAAAxAQBDgAAAABiggAHAAAAADFBgAMAAACAmCDAAQAAAEBMEOAAAAAAICYIcAAAAAAQEwQ4AAAAAIgJAhwAAAAAxAQBDgAAAABiggAHAAAAADFBgAMAAACAmCDAAQAAAEBMEOAAAAAAICYIcAAAAAAQE+acK3YZBjCzg5JeK3Y5hjFd0qFiFwKBY7smD9s0mdiuycM2TSa2a/KwTYtjoXOudrgHIhfgosrM6p1zdcUuB4LFdk0etmkysV2Th22aTGzX5GGbRg9DKAEAAAAgJghwAAAAABATBDj/7il2ARAKtmvysE2Tie2aPGzTZGK7Jg/bNGL4DhwAAAAAxAQ9cAAAAAAQEwQ4H8xslZltM7MdZnZbscuDzMxsvpn9wsy2mtkWM/uUt3yqmT1uZtu931O85WZmX/e27Ytmdn7aa93krb/dzG4q1ntCipmVm9kmM3vUu7/YzJ71tt39ZlblLR/j3d/hPb4o7TU+6y3fZmZXF+mtwGNmk83sITN7xcxeNrOL2Vfjzcz+zDv2bjazH5pZNftq/JjZvWb2ppltTlsW2L5pZheY2Uvec75uZlbYd1h6MmzTr3jH3xfN7EdmNjntsWH3wUxt4kz7OULinONnhB9J5ZJ2SjpFUpWkFyQtL3a5+Mm4vWZLOt+7XSPpVUnLJd0l6TZv+W2S/t67fY2kxySZpIskPestnyppl/d7ind7SrHfXyn/SPq0pB9IetS7/4CkG73b35T0x97tP5H0Te/2jZLu924v9/bfMZIWe/t1ebHfVyn/SPp3SX/o3a6SNJl9Nb4/kuZK2i1prHf/AUkfZV+N34+kyyWdL2lz2rLA9k1Jz3nrmvfcdxf7PSf9J8M2vUpShXf779O26bD7oEZoE2faz/kJ54ceuNGtlLTDObfLOdcp6T5Jq4tcJmTgnNvvnNvo3T4m6WWlGhWrlWosyvt9vXd7taTvuZRnJE02s9mSrpb0uHOuyTl3RNLjklYV7p0gnZnNk3StpG97903SFZIe8lYZvE37tvVDkt7prb9a0n3OuQ7n3G5JO5Tav1EEZjZJqQbFdyTJOdfpnDsq9tW4q5A01swqJI2TtF/sq7HjnPulpKZBiwPZN73HJjrnnnGp1v730l4LIRlumzrn/ss51+3dfUbSPO92pn1w2DbxKP+TEQIC3OjmSmpIu7/XW4aI84bjrJD0rKSZzrn93kNvSJrp3c60fdnu0fJPkv5SUq93f5qko2n/eNK3T/+28x5v9tZnm0bLYkkHJX3XGxr7bTMbL/bV2HLONUr6qqTXlQpuzZI2iH01KYLaN+d6twcvR3F9TKneUCn7bTrS/2SEgACHRDKzCZIelvS/nHMt6Y95Z/yYfjUmzOw9kt50zm0odlkQqAqlhvP8i3NuhaTjSg3L6se+Gi/ed6JWKxXO50gaL3pDE4l9M1nM7HZJ3ZK+X+yywB8C3OgaJc1Puz/PW4aIMrNKpcLb951zj3iLD3jDNuT9ftNbnmn7st2j4xJJ15nZHqWGa1wh6WtKDdOp8NZJ3z792857fJKkw2KbRs1eSXudc8969x9SKtCxr8bXlZJ2O+cOOue6JD2i1P7LvpoMQe2bjTo5VC99OYrAzD4q6T2SPuwFcyn7bXpYmfdzhIAAN7r1kpZ6s+tUKfVF6zVFLhMy8MZhf0fSy865f0x7aI2kvhmwbpL0k7TlH/Fm0bpIUrM3RGSdpKvMbIp3VvkqbxkKzDn3WefcPOfcIqX2v5875z4s6ReSbvBWG7xN+7b1Dd76zlt+ozfz3WJJS5X6Ij2KwDn3hqQGMzvNW/ROSVvFvhpnr0u6yMzGecfivm3KvpoMgeyb3mMtZnaRV08+kvZaKCAzW6XU1xOuc86dSHso0z44bJvY228z7ecIQ7FnUYnDj1IzLL2q1Mw7txe7PPyMuK0uVWpYx4uSnvd+rlFqfPaTkrZLekLSVG99k3S3t21fklSX9lofU+qLuzsk/UGx3xs/TpLerpOzUJ6i1D+UHZIelDTGW17t3d/hPX5K2vNv97b1NjHrWdF/JJ0nqd7bX3+s1Ex17Ksx/pH0N5JekbRZ0n8oNYsd+2rMfiT9UKnvMXYp1Vt+c5D7pqQ6r47slPT/JFmx33PSfzJs0x1Kfaetr730zbT1h90HlaFNnGk/5yecH/M+dAAAAABAxDGEEgAAAABiggAHAAAAADFBgAMAAACAmCDAAQAAAEBMEOAAAAAAICYIcAAAAAAQEwQ4AAAAAIgJAhwAAAAAxMT/D07+YKb6c0SkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "range_future = len(y_test)\n",
    "plt.plot(np.arange(range_future), np.array(y_test),  label='Test data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=y_train.reshape(y_train.shape[0],1)\n",
    "y_test=y_test.reshape(y_test.shape[0],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51934, 30)\n",
      "(51934, 1)\n",
      "(12961, 30)\n",
      "(12961, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "n_features = 1\n",
    "n_seq = 15\n",
    "n_steps = 2\n",
    "X_train = X_train.reshape((X_train.shape[0], n_seq, 1, n_steps, n_features))\n",
    "X_test= X_test.reshape((X_test.shape[0], n_seq, 1, n_steps, n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51934, 15, 1, 2, 1)\n",
      "(51934, 1)\n",
      "(12961, 15, 1, 2, 1)\n",
      "(12961, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv_lst_m2d (ConvLSTM2D)    (None, 1, 1, 64)          33536     \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 33,601\n",
      "Trainable params: 33,601\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(ConvLSTM2D(filters=64, kernel_size=(1,2), activation='relu', input_shape=(n_seq, 1, n_steps, n_features)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def fit_model(model):\n",
    "#     early_stop = keras.callbacks.EarlyStopping(monitor = 'val_loss',\n",
    "#                                                patience = 10)\n",
    "#     history = model.fit(X, y, epochs = 5, verbose=0)\n",
    "#                         #validation_split = 0.2,\n",
    "#                         #batch_size = 16, shuffle = False, \n",
    "#                         #callbacks = [early_stop])\n",
    "#     return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def fit_model(model):\n",
    "    history = model.fit(X_train, y_train, epochs = 5, verbose=0,\n",
    "                     validation_split = 0.2,\n",
    "                     batch_size = 16)\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x15a3d83b7c0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_convlstm=fit_model(model)\n",
    "history_convlstm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_loss (history, model_name):\n",
    "    plt.figure(figsize = (8, 5))\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('Model Train vs Validation Loss for'+ model_name)\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['Train loss', 'Validation loss'], loc='upper right')\n",
    " \n",
    "plot_loss (history_convlstm, 'ConvLSTM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.3067016 ],\n",
       "       [0.3067016 ],\n",
       "       [0.3067016 ],\n",
       "       ...,\n",
       "       [0.08940959],\n",
       "       [0.08920328],\n",
       "       [0.08940233]], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def prediction(model):\n",
    "    prediction = model.predict(X_test)\n",
    "#     prediction = scaler.inverse_transform(prediction)\n",
    "    return prediction\n",
    "prediction= prediction(model)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12961"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.30434783],\n",
       "       [0.30434783],\n",
       "       [0.30434783],\n",
       "       ...,\n",
       "       [0.08695652],\n",
       "       [0.08695652],\n",
       "       [0.08695652]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_future(prediction, model_name, y_test):\n",
    "    plt.figure(figsize=(15, 8))\n",
    "    range_future = len(prediction)\n",
    "    plt.plot(np.arange(range_future), np.array(y_test),  label='Test data')\n",
    "    plt.plot(np.arange(range_future),np.array(prediction),label='Prediction')\n",
    "    plt.title('Test data vs prediction for ' + model_name)\n",
    "    plt.legend(loc='upper left',fontsize=15)\n",
    "    plt.xlabel('Time',fontsize=14)\n",
    "    plt.ylabel('CO',fontsize=14)\n",
    "\n",
    "plot_future(prediction,'ConvLSTM', y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ConvLSTM:\n",
      "Mean Square Error: 0.0001\n",
      "Root Mean Square Error: 0.0115\n",
      "Mean Absolute  Error: 0.0067\n"
     ]
    }
   ],
   "source": [
    "def evaluate_prediction(predictions, actual, model_name):\n",
    "    dif=predictions-actual\n",
    "    errors = np.nan_to_num(dif, nan=0)\n",
    "    mse = np.square(errors).mean()\n",
    "    rmse = np.sqrt(mse)\n",
    "    mae = np.abs(errors).mean()\n",
    "    print(model_name + ':')\n",
    "    print('Mean Square Error: {:.4f}'.format(mse))\n",
    "    print('Root Mean Square Error: {:.4f}'.format(rmse))\n",
    "    print('Mean Absolute  Error: {:.4f}'.format(mae))\n",
    "    return errors, mse, rmse, mae\n",
    "\n",
    "errors, mse, rmse, mae= evaluate_prediction(prediction, y_test, 'ConvLSTM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Error: 0.0001\n",
      "Root Mean Square Error: 0.0115\n",
      "Mean Absolute  Error: 0.0067\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "mae = mean_absolute_error(y_test,prediction)\n",
    "mse = mean_squared_error(y_test,prediction)\n",
    "rmse=sqrt(mse)\n",
    "print('Mean Square Error: {:.4f}'.format(mse))\n",
    "print('Root Mean Square Error: {:.4f}'.format(rmse))\n",
    "print('Mean Absolute  Error: {:.4f}'.format(mae))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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